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offering substantial opportunities for producing sustainable solutions. Despite the
many technical challenges of deriving analytics based on data streams, there are
considerable opportunities for monitoring, managing risks and developing early
warning systems (McSharry
2012
). Furthermore, information harvested from social
media can be used to quantify perceived risks, which are important for motivating
public policy.
Scientifi c modelling provides an invaluable tool for decision-makers. With
regard to risk assessment, models provide a means of investigating historical records
of extreme events, testing hypotheses, forecasting the likelihood of future events
and simulating the infl uence of different variables (McSharry et al.
2013
). There is
a growing need to bring together researchers with expertise in environmental sci-
ence, statistics, physics, mathematics, engineering and economics to develop a mul-
tidisciplinary approach for quantifying risks, analysing the impacts, costs and
benefi ts of competing policies and establishing strategies for increasing resilience.
Policymakers are faced with the immediate task of making important decisions
in the present that will determine how we cope with these challenges over the next
few decades. More multidisciplinary research and cooperation between scientists,
the private sector and government is needed. While decision-makers would prefer to
obtain a simple scenario, it is important to accept that the future is inherently uncer-
tain and no crystal ball exists. A thorough evaluation of all the quantitative and
qualitative information is likely to produce numerous future scenarios. As risk
assessment becomes more important for policymaking, there is a growing emphasis
on promoting sustainability and resilience, which may imply the need to transform
rather than simply adapting or recovering from environmental shocks. It is not only
the direct impact of extreme weather but also the indirect effects of the threat of
disaster that impede economic growth and human development. The insurance
industry is in a prime location to advise and manage the transition towards a more
resilient society.
17.3
Risk Forecasting
The insurance industry is increasingly using forecasting to quantify economic
impacts and evaluate strategies for managing the risks associated with environmen-
tal change. Forecasting is defi ned as attempting to predict the future, and modelling
is a tool used to generate forecasts.
Mathematical and scientifi c models encapsulate information and hypotheses in
an objective and transparent framework (Box 17.1). These models often form the
basis of effective disaster risk management systems and national and international
catastrophe programmes. Parametric insurance, such as index-based insurance,
relies on mathematical models to construct an index for deciding when compensa-
tion is provided and is not based on losses but on pre-determined trigger events (IRI
2009
). This innovative form of insurance is further discussed in Sect.
17.6
.
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